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针对一类不确定非线性动态系统,提出了一种基于神经网络动态补偿的鲁棒模型跟随重构控制策略.该方法吸取了线性模型跟随方法的基本思想,通过引入神经网络在线补偿控制器,以克服系统由故障引起的未建模非线性动态的影响,从而提高模型跟随重构控制的动态性能和稳态精度;并且当系统存在模型不确定性时,其输出仍能精确地跟踪理想模型的输出.文中还给出了特定假设条件下闭环重构控制系统稳定性的严格证明.理论分析和仿真研究表明,所提的方法是有效的并可保证闭环系统具有良好的重构性能. 相似文献
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基于自组织模糊CMAC网络的非线性系统鲁棒自适应跟踪控制 总被引:2,自引:2,他引:2
基于自组织模糊CMAC(SOFCMAC)神经网络,提出了一种非线性模型参考神经网络
增广逆系统鲁棒自适应跟踪控制方法.该方法的特点是通过S0FCMAC神经网络在线修正由
于建模误差、不确定因素等引起的非线性系统逆误差,使得系统输出准确跟踪参考模型输出.
SOFCMAC的权值调整规律由Lyapunov稳定性理论导出.文中证明了非线性闭环系统的稳定
性.仿真例子表明了本文方法的有效性. 相似文献
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针对包含电机动态模型的移动机械臂系统,提出一种鲁棒自适应输出反馈控制方法.将误差符号函数鲁棒积分反馈与神经网络前馈结构相结合用于控制器的设计,然后利用神经网络去逼近机器人和电机系统的不确定项,设计鲁棒项实时补偿网络误差.通过Lyapunov稳定性分析证明闭环系统所有信号半全局一致有界.最后仿真实验表明,控制方法对系统动态不确定性和外界干扰有很好的鲁棒性,可实现移动机械臂的输出反馈跟踪控制. 相似文献
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在工业机械臂系统的跟踪控制过程中,由于其结构和工作环境复杂,导致难以建立精确的系统模型,针对此问题提出了基于多层前馈神经网络的自适应鲁棒控制器.通过神经网络在线估计机械臂系统动力学模型,并在控制器中进行补偿,同时设计了一个在线更新的鲁棒项克服神经网络的重构误差;考虑机械臂实际系统的输出约束,采用障碍李雅普诺夫函数设计控制律并证明系统的稳定性从而使系统满足约束条件.仿真实验结果表明:在约束条件下所提出的控制器能够实现系统的一致最终有界稳定,且跟踪性能良好,并具有很好的抗干扰和自适应能力. 相似文献
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为克服现有近似最优跟踪控制方法只能跟踪连续可微参考输入的局限,本文针对一类具有未知动态的连续时间非线性时不变仿射系统,提出了一种新的基于自适应动态规划的鲁棒近似最优跟踪控制方法.首先采用递归神经网络建立系统模型,然后建立评价神经网络对最优性能指标进行估计,从而得到最优性能指标偏导数的估计值,进而得到近似最优跟踪控制器,最后利用系统输出与参考输入之间的跟踪误差设计鲁棒项对神经网络建模误差进行补偿.分别针对两个非线性系统进行仿真实验,仿真结果表明了所提方法的有效性和优越性. 相似文献
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针对再入段高超飞行器非线性动力学模型存在不确定性和干扰,基于奇异摄动理论提出了鲁棒变结构+动态逆内外环解耦控制方法.为避免在线实时求逆,控制系统的外环基于简化的模型设计自适应滑模变结构控制律,通过反馈干扰观测器在线估计广义干扰量,实现角度的跟踪和闭环系统的稳定,抑止外来干扰.强耦合的姿态动力学内环采用动态逆跟踪角速度指... 相似文献
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不确定非线性系统的模糊鲁棒跟踪控制 总被引:7,自引:0,他引:7
提出了一种基于T-S模糊型的鲁捧自适应跟踪控制方法.整个控制方案在结合所有
的局部线性状态反馈控制器的基础上,引入了基于自适应神经网络的鲁棒控制器.所提出的
模糊自适应鲁棒控制器设计方法不需要求取李亚普诺夫方程的公共解,不要求系统的不确定
性项满足任何匹配条件或约束条件所提出的带有补偿项的完全自适应RBF神经网络,通过
在线自适应调整RBF神经网络的权重、函数中心和宽度,提高了神经网络的学习能力,可以
有效地对消系统的未知不确定性的影响.同时通过自适应补偿项来在线估计神经网络的近似
误差边界,弥补了神经网络的不足.所提出的方案保证了闭环系统的稳定性,有效地提高了
系统的鲁棒性和跟踪性能.仿真实例表明了所提出方法的有效性. 相似文献
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Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks 总被引:1,自引:0,他引:1
Po-Feng Tsai Ji-Zheng Chu Shi-Shang Jang Shyan-Shu Shieh 《Journal of Process Control》2003,13(5):423-435
Chemical processes are nonlinear. Model based control schemes such as model predictive control are highly related to the accuracy of the process model. For a highly nonlinear chemical system, it is clear to implement a nonlinear empirical model, such as artificial neural network model, should be superior to a linear model such as dynamic matrix model. However, unlike linear systems, the accuracy of a nonlinear empirical model strongly depends on its original data or training data based on how the model is built up. A regional-knowledge index is proposed in this study and applied in the analysis of dynamic artificial neural network models in process control. New input patterns that imply extrapolations and thus unreliable prediction by an artificial neural network model can be recognized from a significant decrease in the regional-knowledge index. To tackle the extrapolation problem and assure stability of the control system, we propose to run a neural adaptive controller in parallel with a model predictive control. A coordinator weights the outputs of these two controllers to make the final control decision. The present state of the controlled process and the model fitness to the present input pattern determine the weightings of the controller's output. The proposed analysis method and the modified model predictive control architecture have been applied to a neutralization process and excellent control performance is observed in this highly nonlinear system. 相似文献
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A. Rahideh A.H. Bajodah M.H. Shaheed 《Engineering Applications of Artificial Intelligence》2012,25(6):1289-1297
This paper investigates the development and experimental implementation of an adaptive dynamic nonlinear model inversion control law for a Twin Rotor MIMO System (TRMS) using artificial neural networks. The TRMS is a highly nonlinear aerodynamic test rig with complex cross-coupled dynamics and therefore represents the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. The proposed on-line learning algorithm updates the weights and biases of the neural network using the error between the set-point and the real output. The real-time response of the method shows a satisfactory tracking performance in the presence of inversion errors caused by model uncertainty. The approach is therefore deemed to be suitable to apply real-time to other nonlinear systems with necessary modifications. 相似文献
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针对具有典型非线性特性的多关节机器人轨迹跟踪控制问题,提出一种基于径向基函数(RBF)神经网络的固定时间滑模控制方法.首先,基于凯恩方法建立包括系统模型不确定性以及外部干扰在内的多关节机器人动力学模型;然后,根据机器人动力学模型设计一种固定时间收敛的滑模控制器, RBF神经网络用来逼近系统模型中的不确定性项,并利用Lyapunov理论证明该系统跟踪误差能在固定时间内收敛;最后,对特定型号的多关节机器人虚拟样机进行仿真分析,结果表明:与基于RBF神经网络的有限时间滑模控制器相比,所提出控制器具有良好的跟踪性能且能保证系统状态在固定时间内收敛. 相似文献
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Chih-Min Lin Chin-Hsu Leng Chun-Fei Hsu Chiu-Hsiung Chen 《Neural computing & applications》2009,18(6):567-575
Linear ultrasonic motor (LUSM) has much merit, such as high precision, fast control dynamics and large driving force, etc.;
however, the dynamic characteristic of LUSM is nonlinear and the precise dynamic model of LUSM is difficult to obtain. To
tackle this problem, this study presents a robust neural network control (RNNC) system for LUSM to track a reference trajectory
with L
2 robust tracking performance. The developed RNNC system is composed of a neural network controller and a robust controller.
The neural network controller is the principal controller used to mimic an ideal controller and the robust controller is adopted
to achieve L
2 robust tracking performance. The developed RNNC system is then applied to control an LUSM. Experimental results show that
the developed RNNC system can achieve favorable tracking performance with unknown of LUSM model. 相似文献
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A new method for the control of discrete nonlinear dynamic systems using neural networks 总被引:6,自引:0,他引:6
A new controller design method for nonaffine nonlinear dynamic systems is presented in this paper. An identified neural network model of the nonlinear plant is used in the proposed method. The method is based on a new control law that is developed for any discrete deterministic time-invariant nonlinear dynamic system in a subregion Psi(x), of an asymptotically stable equilibrium point of the plant. The performance of the control law is not necessarily dependent on the distance between the current state of the plant and the equilibrium state if the nonlinear dynamic system satisfies some mild requirements in Psi(x). The control law is simple to implement and is based on a novel linearization of the input-output model of the plant at each instant in time. It can be used to control both minimum phase and nonminimum phase nonaffine nonlinear plants. Extensive empirical studies have confirmed that the control law can be used to control a relatively general class of highly nonlinear multiinput-multioutput (MIMO) plants. 相似文献
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Tong
Ma 《国际强度与非线性控制杂志
》2020,30(12):4565-4583
》2020,30(12):4565-4583
This paper synthesizes a filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. The nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF)‐based neural network incorporated with a piecewise constant adaptive law, where the adaptive law will generate adaptive parameters by solving the error dynamics between the real system and the state predictor with the neglection of unknowns. The combination of GRBF‐based neural network and piecewise constant adaptive law relaxes hardware limitations (CPU). A filtering control law is designed to handle the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. The matched uncertainties are cancelled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. Since the virtual reference system defines the best performance that can be achieved by the closed‐loop system, the uniform performance bounds are derived for the states and control signals via comparison. To validate the theoretical findings, comparisons between the model reference adaptive control method and the proposed filtering adaptive neural network control architecture with the implementation of different sampling time are carried out. 相似文献